calculate_noise_threshold_method_statistics {noisyr}

Function to tabulate statistics for different methods of calculating the noise threshold

Description

This function is used to tabulate and compare different combinations of similarity threshold and method to calculate the noise threshold for a given expression matrix.

Usage

calculate_noise_threshold_method_statistics(
  expression,
  similarity.threshold.sequence = 0.25,
  method.chosen.sequence = noisyr::get_methods_calculate_noise_threshold(),
  dump.stats = NULL,
  ...
)

Arguments

expression

either an expression summary (as calculated by calculate_expression_similarity_counts or calculate_expression_similarity_transcript), which should be a list with 3 slots: expression.matrix, expression.levels, expression.levels.similarity; alternatively, just an expression matrix; only density based methods are available for the latter case

similarity.threshold.sequence

similarity (correlation or inverse distance) threshold(s) to be used to find corresponding noise threshold; can be a single value or a numeric vector; the default, 0.25 is usually suitable for the Pearson correlation (the default similarity measure)

method.chosen.sequence

methods to use to calculate the noise thresholds, must be a subset of get_methods_calculate_noise_threshold; defaults to all

dump.stats

name of csv to export different thresholds calculated (optional)

...

other arguments (for the boxplot methods) passed to calculate_noise_threshold

Value

A tibble containing information on noise thresholds calculated using the input similarity thresholds and methods (optionally written in a csv file). The columns list the chosen method and similarity threshold, the minimum, mean, coefficient of variation, and maximum of the noise thresholds, and all the noise thresholds concatenated as a string.

See Also

calculate_noise_threshold

Examples

expression.summary <- calculate_expression_similarity_counts(
    expression.matrix = matrix(1:100, ncol=5),
    method = "correlation_pearson",
    n.elements.per.window = 3)
calculate_noise_threshold_method_statistics(expression.summary)